Automated collision avoidance in medical environments

ABSTRACT

An apparatus for automated collision avoidance includes a sensor configured to detect an object of interest, predicting a representation of the object of interest at a future point in time, calculating an indication of a possibility of a collision with the object of interest based on the representation of the object of interest at the future point in time, and executing a collision avoidance action based on the indication.

FIELD

The aspects of the disclosed embodiments relate generally to objectdetection, and more particularly to automated collision prediction andavoidance in a medical environment.

BACKGROUND

Existing systems in medical environments may rely on ultrasonic andcontact sensors to detect collisions. Ultrasonic sensors are usuallymounted on moving parts of devices such as robotic arms and can detectobjects within a relatively close range. When an object gets within apredetermined range the device can stop its movement. A drawback ofultrasonic sensors is the limited field of view, which provides coveragefor a very small region.

Similar to ultrasonic sensors, contact sensors are usually mounted onthe moving part of the device. Contact sensors rely on the detection ofphysical contact with the object to prevent severe collisions. Suchcontact sensors tend to have an even smaller coverage area and aretypically used as a last layer of protection, after a collision hasalready occurred. These approaches do not provide a sufficient amount ofcollision avoidance, and do not provide a required degree of safetyassurance to personnel in the medical environment.

Accordingly, it would be desirable to provide methods and apparatus thataddress at least some of the problems described above.

SUMMARY

The aspects of the disclosed embodiments are directed to automatedcollision avoidance in the medical environment. This and otheradvantages of the disclosed embodiments are provided substantially asshown in, and/or described in connection with at least one of thefigures, as set forth in the independent claims. Further advantageousmodifications can be found in the dependent claims.

According to a first aspect, the disclosed embodiments are directed toan apparatus for automated collision avoidance in a medical environment.In one embodiment the apparatus includes a sensor or sensors configuredto detect at least one object of interest in a space; predict arepresentation of the object of interest in the space at a future pointin time; calculate an indication of a possibility of a collision of theobject of interest with another object in the space based on therepresentation of the object of interest at the future point in time,and execute a collision avoidance action based on the indication.

In a possible implementation form, the apparatus is mounted on ordisposed in connection with a robotic system configured to move in amedical environment.

In a possible implementation form the apparatus includes more than onesensor or a sensor array.

In a possible implementation form of the apparatus, the sensor is avisual sensor.

In a possible implementation form, the sensor includes one or morecameras.

In a possible implementation form of the apparatus, the sensor is one ormore of an RGB sensor, a depth sensor or an infrared sensor.

In a possible implementation form, the apparatus includes one or more ofa stationary sensor or a mobile sensor.

In a possible implementation form, the representation of the object ofinterest can be one or more of a 3D point cloud, a mesh representationor a voxel representation.

In a possible implementation form the apparatus is configured to predictthe representation of the object of interest at a future point of timebased on current motion information of the object of interest that isestimated based on representations of one or more of a current state ofthe object of interest or a previous state.

In a possible implementation form the motion information is one or moreof a 2D velocity, a 3D velocity or an acceleration of the object ofinterest.

In a possible implementation form the calculated indications areprojections of the representation of the object of interest, and a partof the robotic system, onto different 2D planes (e.g., x-y plane, x-zplane and y-z plane) respectively.

In a possible implementation form the processor is configured toevaluate the projections and determine the likelihood of a collisionbetween a part of the robotic system and the object of interest within apre-determined time window, based on a pre-determined or learned metric,such as a union of intersection between the part of robotic system andthe object of interest on each projection plane.

In a possible implementation form, the apparatus is configured tocompare an image of the space acquired at a time t_(n) to an image ofthe space acquired at a time t_(n-1); calculate a movement of one ormore pixels between the different images and calculate the possibilityof the collision based on the pixel movement.

In a possible implementation form, the apparatus is configured tocapture images of the space at different times in a continuous manner,compare the different images to determine movement of pixels between thedifferent images and calculate the possibility of collision based on thepixel movement.

In a possible implementation form the apparatus is configured toestimate a moving direction and a velocity for each pixel or point inand between the different images of the space.

In a possible implementation form the apparatus is configured to predicta next position for a pixel or point in the space.

In a possible implementation form, the apparatus is integrated with anAngiography suite. The motion trajectory of the c-arm of the Angio suiteis pre-calculated and evaluated by the processor, together with thepredicted representations of the patient, patient support and otherequipment and persons in the Angio suite in a future point of timewithin the window of the trajectory arriving at a consolidated decisionsuch as whether the c-arm should proceed with the motion, re-plan thetrajectory, stop the motion, or request manual intervention.

In a possible implementation form the apparatus is integrated with aradiation therapy system. The apparatus is configured to prevent therotating treatment head from colliding with the patient, the patientsupport or other equipment.

In a possible implementation form, the apparatus is integrated with asurgical robotic system. The apparatus is configured to provide supportand feedback on the trajectory planning of the robotic arms, as well assafety assurance to the patient, medical personnel, as well asequipment, throughout the operation.

In a possible implementation form, the apparatus is integrated with amedical scanner such as a computed tomography (CT) or magnetic resonance(MR) scanner. The apparatus is configured to provide prediction andwarnings when there is an indication that the patient may be or iscolliding with the scanner or other equipment in the scanning room.

In a possible implementation form, the apparatus is integrated with anx-ray system. The x-ray system can be either ceiling mounted or floormounted. The apparatus is configured to predict and prevent collisionsbetween a patient and a part of the x-ray system (e.g., robotic arms,the x-ray tube or the flat panel) during the scanning or positioningprocess. The apparatus is configured to guide the x-ray robotic armsduring navigation and path planning process to avoid obstacles.

According to a second aspect, the disclosed embodiments are directed toa method. In one embodiment, the method includes detecting an object ofinterest; predicting a representation of the object of interest at afuture point in time; calculating an indication of a possibility of acollision with the object of interest based on the representation of theobject of interest at the future point in time, and executing acollision avoidance action based on the indication.

In a possible implementation form the method includes predicting therepresentation of the object of interest at a future point of time basedon current motion information of the object of interest that isestimated based on representations of one or more of a current state ofthe object of interest or a previous state.

In a possible implementation form, the method includes calculating theindications by projecting representations of the object of interest, anda part of the equipment or robotic system, onto different 2D planes,respectively.

According to a third aspect, the disclosed embodiments are directed to acomputer program product embodied on a non-transitory computer readablemedium, the computer program product comprising computer instructionsthat, when executed on at least one processor of a system or anapparatus, is configured to perform the possible implementation formsdescribed herein.

According to a fourth aspect, the disclosed embodiments are directed toa device comprising means for performing the possible implementationforms described herein.

These and other aspects, implementation forms, and advantages of theexemplary embodiments will become apparent from the embodimentsdescribed herein considered in conjunction with the accompanyingdrawings. It is to be understood, however, that the description anddrawings are designed solely for purposes of illustration and not as adefinition of the limits of the disclosed invention, for which referenceshould be made to the appended claims. Additional aspects and advantagesof the invention will be set forth in the description that follows, andin part will be obvious from the description, or may be learned bypractice of the invention. Moreover, the aspects and advantages of theinvention may be realized and obtained by means of the instrumentalitiesand combinations particularly pointed out in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following detailed portion of the present disclosure, theinvention will be explained in more detail with reference to the exampleembodiments shown in the drawings, in which:

FIG. 1 is a block diagram of an apparatus incorporating aspects of thedisclosed embodiments.

FIG. 2 is a block diagram of an exemplary environment for an apparatusincorporating aspects of the disclosed embodiments.

FIG. 3 is a flowchart illustrating an exemplary process flowincorporating aspects of the disclosed embodiments.

DETAILED DESCRIPTION OF THE DISCLOSED EMBODIMENTS

The following detailed description illustrates exemplary aspects of thedisclosed embodiments and ways in which they can be implemented.Although some modes of carrying out the aspects of the disclosedembodiments have been disclosed, those skilled in the art wouldrecognize that other embodiments for carrying out or practising theaspects of the disclosed embodiments are also possible.

Referring to FIG. 1 , a schematic block diagram of an exemplaryapparatus 100 incorporating aspects of the disclosed embodiments isillustrated. The aspects of the disclosed embodiments are generallydirected to automated collision avoidance in a medical environment. Theapparatus 100 is configured detect a potential collision betweendifferent object 12 and at least on other object 14, in a space orenvironment 10, such as a room. For purposes of the description herein,the environment will be referred to as a “space.”

The object 12, and at least one other object 14, also referred to asobject(s) of interest, can include one or more of equipment or people.The aspects of the disclosed embodiments are configured to avoidcollisions between different pieces of equipment, as well as equipmentand people, particularly in a medical environment. The apparatus 100will advantageously lower the risk of damage to equipment and injury topeople in an environment, such as a medical environment.

Although the aspects of the disclosed embodiments will be describedherein with respect to collisions between object 12 and at least oneother object 14, the aspects of the disclosed embodiments are not solimited. For example, one of the objects 12, 14 could be a wall or otherstructure of the environment 10. In this embodiment, the apparatus 100can be configured to predict such a collision.

For the purposes of the disclosure herein, the aspects of the disclosedembodiment will generally be described with respect to object 12. Inalternate embodiments, the aspects of the disclosed embodiments asdescribed herein can equally apply to the at least one object 14.

Referring to FIG. 1 , the apparatus 100 generally comprises a sensor 108that is communicatively connected or coupled to a processor 102. Thesensor 108 is generally configured to detect or capture images of one ormore objects 12, 14 that are within a field of view of the sensor 108.In one embodiment, the sensor(s) 108 is configured to capture images ofthe space 10. Although only one sensor 108 is generally referred toherein, the aspects of the disclosed embodiments are not so limited. Inalternate embodiments the apparatus 100 can include any suitable numberof sensors 108, other than including one. In one embodiment, the sensor108 can comprise an array of sensors 108. The sensors 108 in such anarray can be communicatively coupled together, or a sensor 108 can beconfigured to communicate with the processor 102 as is generallydescribed herein.

In the example of FIG. 1 , the processor 102 is configured to predict arepresentation of the object of interest in the space at a future pointof time based on the detection of the object 12 or the images capturedby the sensor 108. Using the predicted representation, the processor 102is configured to calculate an indication of a possibility of a collisionof the object 12 with the at least one other object 14 in the space 10and execute a collision avoidance action based on the indication. Theaspects of the disclosed embodiments advantageously use visual sensorsto automatically predict and detect collisions between different objectsin an environment, such as equipment and people.

In one embodiment, the sensor 108 comprises an image or imaging sensor,such as a visual sensor. In alternate embodiments, the sensor 108 cancomprises any suitable type of visual sensor, such as a camera, forexample. Other examples of the sensor 108 can include, but are notlimited to, a red-green-blue (RGB) sensor, a depth sensor or an infraredsensor. The sensor 108 can also be a stationary sensor or a mobilesensor, or a combination thereof.

In one embodiment, the sensor 108 can be remotely located from theapparatus 100. For example, if the apparatus 100 is disposed in a room,the sensor 108 could be disposed in the room away from the apparatus100. In this manner, the sensor 108, or one or more sensor(s) 108, canbe uniquely positioned or disposed to provide accurate information withrespect to different objects of interest 10 within the room. In thisexample, the sensor(s) 108 and the processor 102 can be communicativelycoupled by a suitable communication network.

The processor 102 is generally configured to detect one or more of theobjects 12, 14 based on the image data provided by the sensor(s) 108.Any suitable image detection algorithm can be used to detect objects inan image.

In one embodiment, the apparatus 102 is configured to predict arepresentation of the object 12 in the space 10 at a future point intime based on the image data provided by the sensor 108. In this manner,the processor 102 is configured to identify, or predict, where, forexample, object 12 will be positioned relative to the at least one otherobject 14 in the space 10 at a later point in time, or time point, inorder to predict the possibility of collision of objects 12 and 14.

In one embodiment, the prediction of the representation of the object 12at a future point of time is based on current motion information of theobject 12. For example, the sensor 108 is configured to gather motioninformation corresponding to object 12. This can include for example,capturing a series of images of the object 12, or the space 10 in whichthe object 12 is disposed. As noted above, the prediction describedherein can be equally applied to the at least one other object 14.

For example, in one embodiment where the sensor 108 is a camera, thecamera can capture images of the space 10 or object 12 at different timepoints t_((n)). The processor 102 can be configured to compare an imageor images captured at time point t_((n)) with an image captured at timepoint t_((n−1)). The differences in the different images can be used topredict the representation of the object 12 at a next time pointt_((n−1)).

In one embodiment, the processor 102 is configured to determine orcalculate pixel movement between the image at time point t_((n)) and theimage at time point t_((n−1)). By identifying or calculating themovement of pixels from one image to a next image, the processor 102 candetermine or identify the movement of the object 12, or any other object14, in the image, as well as a magnitude of such movement(s). In oneembodiment, an optical flow algorithm can be used to calculate pixelmovements between different images or different timespans. By doing thisfor consecutive time steps T_((n)), T_((n−1)), the moving direction andvelocity of each point or pixel in the image of the space 10 can beestimated. In this manner, a prediction can be made as to where eachpoint or pixel in the space is going to be in a next time stepT_((n+1)). A determination of any collision between different objects12, 14 in the space 10 can then be made based on the prediction.

In one embodiment, the processor 102 is configured to determine motioninformation associated with the object 12. This motion information caninclude for example, a two-dimensional (2D) or three dimensional (3D)velocity of the object 12, as well as an acceleration of the object 12.In one embodiment, the motion information can be estimated based onrepresentations of one or more of a current state or a previous state ofthe object 12. It will be understood that the sensor(s) 108 arecontinually detecting and updating information on the object(s) ofinterest 12 in the environment.

The processor 102 is configured to use the information from the sensor108 to predict a collision between the object 12 and at least one otherobject 14, as well as initiate collision avoidance actions.

In one embodiment, the processor 102 is configured to extract arepresentation of the object 12 from the information captured by thesensor(s) 108. The representation can comprise for example athree-dimensional (3D) cloud representation, a mesh or a voxelrepresentation. In alternate embodiments, the representation can be anysuitable representation that enables the processor 102 to identifycharacteristics of the object 12. These characteristics can include, butare not limited to, a geometric shape of the object 12, a movement ormotion of the object 12, a direction, acceleration, velocity and speedof such movement and a relative position of the object 12 with respectto at least one other object. In alternate embodiments, thecharacteristics can include any suitable characteristics that can beused to predict a collision between the object 12 and at least one otherobject.

In one embodiment, the processor 102 is configured to calculate anddetermine one or more indications which can be used to gauge thepossibility of a collision between the object 12 and another object suchas object 14, at a given point of time, based on the predictedrepresentation of the object 12. The calculated indications can beprojections of the representation of the object 12 and at least a partof another object 14, onto different two-dimensional (2D) planes. The 2Dplanes can include for example the x-y plane, x-z plane and y-z plane.

In one embodiment, the processor 102 is configured to evaluate thedifferent projections of the representation of the object 12 and the atleast one other object 14, and determine a likelihood of a collision. Inone embodiment, the likelihood of collision between a part of the object12 and a part of the at least one other object 14 can be determinedwithin a pre-determined time window.

The likelihood of collision can be determined based on a pre-determinedor learned metric. This learned metric can include for example, but isnot limited to a union of intersection between at least part of theobject 12 and at least part of the at least one other object 14 on orfor each of the different projection planes.

Once the likelihood of collision is evaluated or predicted, theprocessor 102 is configured to initiate or execute responsive actions.Such responsive actions can include, but are not limited to, stoppingmovement of the objects 12 or 14, changing or adjusting a movement,initiating an alarm or warning for example. In alternate embodiments,any suitable collision avoidance acts or actions can be taken orinitiated.

Referring to FIG. 2 , the apparatus 100 is configured to be disposed orimplemented in an environment 200, such as a medical environment ormedical imaging environment. In this example, the apparatus 100 isdisposed on or in connection with a medical imaging device or equipment210. Alternatively, the apparatus 100 can be a standalone device.

In one embodiment, the apparatus 100 can also be communicatively coupledto the medical device 210. Examples of such medical systems can include,but are not limited to, x-ray systems, medical resonance imaging (MRI)systems, computed tomography (CT) systems, and surgical robotic systems.In alternative embodiments, the apparatus 100 can be embodied in or partof any suitable device or system where collision avoidance is desired.

In one embodiment, the device 210 is, includes or is connected to arobotic device or system. The aspects of the disclosed embodiments canbe implemented in conjunction with systems or devices that areconfigured to move or rotate.

In the example of FIG. 2 , the sensors 108 comprise the cameras 204a-204 d. The cameras 204 a-204 d are configured to communicate with theapparatus 100 in this example. As is described herein, the cameras 204a-204 d are used to detect the object 214 relative to the equipment 210.A representation of the object 214 and/or the equipment at a next orfuture point of can be predicted and used to determine a possibility ofa collision between the equipment 210 and the object of interest 214, asis generally described.

FIG. 3 is a flowchart illustrating an exemplary method incorporatingaspects of the disclosed embodiments. In this example, the methodincludes detecting 302 an object of interest in the environment. Arepresentation of the object of interest at a next or future point oftime is predicted 304. In one embodiment, the prediction therepresentation of the object of interest at a future point of time isbased on current motion information of the object of interest that isestimated based on representations of one or more of a current state ofthe object of interest or a previous state.

An indication of a possibility of a collision of an apparatus with theobject of interest based on the representation of the object of interestat the future point in time is calculated 306. In one embodimentcalculating the indications includes projecting representations of theobject of interest, and a part of the equipment onto different 2Dplanes, respectively. A collision avoidance action based on theindication is executed 308.

The apparatus 100 can be disposed or implemented in many differentenvironments 12. An exemplary system 200 incorporating aspects of thedisclosed embodiments is shown in FIG. 2 . For example, in oneembodiment, the apparatus 100 is integrated with an equipment 10, suchas an Angio suite. The motion trajectory of the c-arm of the Angio suiteis pre-calculated and evaluated by the processor 102, together with thepredicted representations of the patient, patient support and otherequipment and persons in the suite at a future point of time. Thepredicted representations can be made within a window of the trajectoryof the c-arm. The processor 102 in this example can be configured todetermine whether the c-arm should proceed with the motion, re-plan thetrajectory, stop the motion, or request manual intervention.

In an embodiment, the apparatus 100 is integrated with a radiationtherapy system. The apparatus 100 in this example is configured toprevent the rotating treatment head from colliding with the patient, thepatient support or other equipment.

In an embodiment, the apparatus 100 is integrated with a surgicalrobotic system. The apparatus 100 is configured to provide support andfeedback on the trajectory planning of the robotic arms of the system.The apparatus 100 can also provide safety assurance for the patient,medical personnel, as well as the associated equipment, throughout theoperation.

In an embodiment, the apparatus 100 is integrated with a medical scannersuch as CT or MR. In this example, the apparatus 100 is configured toprovide prediction and warnings when there is an indication of acollision between the patient and the scanner or other equipment in thescanning room.

In an embodiment, the apparatus 100 is integrated with an x-ray system.The x-ray system can be either ceiling mounted or floor mounted. Theapparatus 100 is configured to predict and prevent collisions between,for example, a patient and a part of the x-ray system. This can includethe robotic arms, the x-ray tube or the flat panel, for example, duringthe scanning or positioning process. The apparatus 100 is alsoconfigured to guide the x-ray robotic arms during navigation and pathplanning process to avoid obstacles.

In one embodiment, the apparatus 100 shown in FIG. 1 , generallycomprises a computing device. The computing device can comprise orinclude any suitable computer or computing arrangement.

In one embodiment, the processor 102 comprises a hardware processor.Although only one processor 102 is generally described herein, theaspects of the disclosed embodiments are not so limited. In alternateembodiments, the apparatus 100 can include any suitable number ofprocessors 102.

The apparatus 100 generally includes suitable logic, circuitry,interfaces and/or code that is configured to receive the informationfrom the sensor(s) 108 and process the information as is generallydescribed herein.

The processor 102 generally includes suitable logic, circuitry,interfaces and/or code that is configured to process the information anddata as is generally described herein. The processor 102 is configuredto respond to and process instructions that drive the apparatus 100.Examples of the processor 102 include, but are not limited to, amicroprocessor, a microcontroller, a complex instruction set computing(CISC) microprocessor, a reduced instruction set (RISC) microprocessor,a very long instruction word (VLIW) microprocessor, or any other type ofprocessing circuit. Optionally, the processor 102 may be one or moreindividual processors, processing devices and various elementsassociated with a processing device that may be shared by otherprocessing devices. Additionally, the one or more individual processors,processing devices and elements are arranged in various architecturesfor responding to and processing the instructions that drive the system100. The apparatus 100 can include any suitable components or devicesthat are needed to carry out the processes described herein, such as amemory or storage, for example.

In one embodiment, the apparatus 100 can comprise or be part of astandalone computing device, in communication with, or part of, theequipment 10. In one embodiment, the apparatus 100 will include or beconnected to the machine learning models needed to carry out the aspectsof the disclosed embodiments described herein.

In the example of FIG. 1 , the apparatus 100 also includes or iscommunicatively coupled to a memory 104. Although not shown, theapparatus 100 could be communicatively coupled to network or networkinterface to enable communication with the components and devices of theapparatus 100.

The memory 104 may comprise suitable logic, circuitry, interfaces,and/or code that may be configured to store instructions executable bythe processor 102. The memory 104 is further configured to store thesensor information, state information and predictions. The memory 104may be further configured to store operating systems and associatedapplications of the processor 102. Examples of implementation of thememory 104 may include, but are not limited to, Random Access Memory(RAM), Read Only Memory (ROM), Hard Disk Drive (HDD), Flash memory,and/or a Secure Digital (SD) card. A computer readable storage medium ofa computer program product for providing a non-transient memory mayinclude, but is not limited to, an electronic storage device, a magneticstorage device, an optical storage device, an electromagnetic storagedevice, a semiconductor storage device, or any suitable combination ofthe foregoing.

The aspects of the disclosed embodiments are directed to collisionavoidance in an environment, such as a medical environment. Visualsensors are used to provide large enough coverage and early detection ofpotential collision to further lower the risk of damage to persons anddevices in the medical environment.

Various embodiments and variants disclosed above, with respect to theaforementioned system 100, apply mutatis mutandis to the method. Themethod described herein is computationally efficient and does not causeprocessing burden on the processor 102.

Modifications to embodiments of the aspects of the disclosed embodimentsdescribed in the foregoing are possible without departing from the scopeof the aspects of the disclosed embodiments as defined by theaccompanying claims. Expressions such as “including”, “comprising”,“incorporating”, “have”, “is” used to describe and claim the aspects ofthe disclosed embodiments are intended to be construed in anon-exclusive manner, namely allowing for items, components or elementsnot explicitly described also to be present. Reference to the singularis also to be construed to relate to the plural.

Thus, while there have been shown, described and pointed out,fundamental novel features of the invention as applied to the exemplaryembodiments thereof, it will be understood that various omissions,substitutions and changes in the form and details of devices and methodsillustrated, and in their operation, may be made by those skilled in theart without departing from the spirit and scope of the presentlydisclosed invention. Further, it is expressly intended that allcombinations of those elements, which perform substantially the samefunction in substantially the same way to achieve the same results, arewithin the scope of the invention. Moreover, it should be recognizedthat structures and/or elements shown and/or described in connectionwith any disclosed form or embodiment of the invention may beincorporated in any other disclosed or described or suggested form orembodiment as a general matter of design choice. It is the intention,therefore, to be limited only as indicated by the scope of the claimsappended hereto.

What is claimed is:
 1. An apparatus for automated collision avoidance,the apparatus comprising a hardware processor configured to: detect anobject of interest within a space based on image data captured by asensor; predict a representation of the object of interest in the spaceat a future point in time; calculate an indication of a possibility of acollision of the object of interest with at least one other object inthe space based on the representation of the object of interest at thefuture point in time; and execute a collision avoidance action based onthe indication.
 2. The apparatus according to claim 1, wherein thehardware processor is further configured to predict the representationof the object of interest in the space at the future point of time basedon motion information of the object of interest.
 3. The apparatusaccording to claim 2 wherein the hardware processor is configured todetermine the motion information of the object of interest based on arepresentation of one or more of a current state of the object ofinterest or a previous state of the object of interest.
 4. The apparatusaccording to claim 1, wherein the hardware processor is configured tocalculate the indication by: projecting the representation of the objectof interest and the at least one other object onto different 2D planes;evaluate the projection; and determine a likelihood of a collisionbetween the object of interest and the at least one other object basedon a union of intersection between the object of interest and the atleast one other object on the different 2D projection planes.
 5. Theapparatus according to claim 4, wherein the device is a robotic device,the apparatus being disposed in connection with the robotic device 6.The apparatus according to claim 1 wherein the sensor is a visualsensor.
 7. The apparatus according to claim 1, wherein the sensor is oneor more of a stationary sensor or a mobile sensor.
 8. The apparatusaccording to claim 1 wherein the sensor is one or more of a RGB sensor,a depth sensor or and infrared sensor.
 9. The apparatus according toclaim 1, wherein the representation of the object of interest comprisesa 3D point cloud, a mesh or a voxel.
 10. The apparatus according toclaim 1, wherein the space is a medical imaging environment and one ormore of the object of interest and the at least one other object is arobotic system.
 11. The apparatus according to claim 1, the apparatus isconfigured to: compare an image of the space acquired by the sensor at atime t_((n)) to an image of the space acquired by the sensor at a timet_((n−1)); calculate a movement of one or more pixels between the imageof the space acquired at the time t_((n)) to the image of the spaceacquired at the time t_((n−1)); and calculate the possibility of thecollision based on the movement of one or more pixels.
 12. The apparatusaccording to claim 12, wherein the apparatus is further configured tocause the sensor to capture images of the space at different timest_((n)) in a continuous manner, and wherein the hardware processor isfurther configured to compare different images to determine movement ofpixels between the different images and calculate the possibility ofcollision based on the pixel movement.
 13. The apparatus according toclaim 12, wherein the hardware processor is configured to estimate amoving direction and a velocity for each pixel in the different images.14. A computer implemented method comprising: detecting an object ofinterest within a space based on image data captured by a sensor;predicting a representation of the object of interest in the space at afuture point in time; calculating an indication of a possibility of acollision of the object of interest with at least one other object inthe space based on the representation of the object of interest at thefuture point in time; and executing a collision avoidance action basedon the indication.
 15. The computer implemented method according toclaim 14, wherein the method further comprises predicting therepresentation of the object of interest at the future point of timebased on current motion information of the object of interest that isbased on a representation of one or more of a current state of theobject of interest or a previous state of the object of interest. 16.The computer implemented method according to claim 14, whereincalculating the indication of the possibility of collision furthercomprises: comparing a first image of the space captured by the sensorat a first time with a second image of the space captured by the sensorat a second time; calculate a movement of pixels associated with theobject of interest from the first image to the second image; andcalculate the indication of the possibility of the collision based onthe movement of pixels associated with the object of interest relativeto the at least one other object.
 17. The computer implemented methodaccording to claim 14, wherein the method further comprises calculatingthe indication by: projecting the representation of the object ofinterest and a representation of the at least one other object ontodifferent 2D planes; evaluating the projection; and determining alikelihood of a collision between the at least one other object and theobject of interest based on a union of intersection between the at leastone other object and the object of interest on the different 2Dprojection planes.
 18. The computer implemented method according toclaim 14, wherein the method comprises: comparing an image of the spaceacquired by the sensor at a time t_((n)) to an image of the spaceacquired by the sensor at a time t_((n−1)); calculating a movement ofone or more pixels between the image of the space acquired at the timet_((n)) to the image of the space acquired at the time t_((n−1)); andcalculating the possibility of the collision based on the movement ofone or more pixels.
 19. The computer implemented method according toclaim 18, wherein the method further comprises: capturing images of thespace at different times t_((n)) in a continuous manner; comparingdifferent images to determine movement of pixels between the differentimages; and calculating the possibility of collision based on the pixelmovement.
 20. A computer program product comprising a non-transitorycomputer-readable medium having machine-readable instructions storedthereon, which when executed by a computer causes the computer toexecute the method according to claim 14.